Modulation spectral features for robust far-field speaker identification
IEEE Transactions on Audio, Speech, and Language Processing
TSD'11 Proceedings of the 14th international conference on Text, speech and dialogue
A new hybrid and dynamic fusion of multiple experts for intelligent porch system
Expert Systems with Applications: An International Journal
Maximum Likelihood Acoustic Factor Analysis Models for Robust Speaker Verification in Noise
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
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In this paper, we study robust speaker recognition in far-field microphone situations. Two approaches are investigated to improve the robustness of speaker recognition in such scenarios. The first approach applies traditional techniques based on acoustic features. We introduce reverberation compensation as well as feature warping and gain significant improvements, even under mismatched training-testing conditions. In addition, we performed multiple channel combination experiments to make use of information from multiple distant microphones. Overall, we achieved up to 87.1% relative improvements on our Distant Microphone database and found that the gains hold across different data conditions and microphone settings. The second approach makes use of higher-level linguistic features. To capture speaker idiosyncrasies, we apply n-gram models trained on multilingual phone strings and show that higher-level features are more robust under mismatching conditions. Furthermore, we compared the performances between multilingual and multiengine systems, and examined the impact of a number of involved languages on recognition results. Our findings confirm the usefulness of language variety and indicate a language independent nature of this approach, which suggests that speaker recognition using multilingual phone strings could be successfully applied to any given language.